Travel control apparatuses of a vehicle for proxying/assisting driving of a driver have been known, such as for example automatic following control apparatuses for making a vehicle follow a vehicle in front by automatically controlling an engine, an automatic transmission, a brake device and so on without reference to driving operations of the driver. Other instances are automatic steering apparatuses for controlling a tire angle so that the vehicle travels along a travel lane of the road, and automatic garage parking apparatuses for carrying out parking of a car by automatically controlling the engine, brake device and steering device and so on.
In a travel control apparatus of this kind, it is aimed that the vehicle to be controlled so that the travel state of the vehicle during proxying/assisting driving of the driver becomes a travel state of the liking of the driver. For that purpose, the idea is considered of setting, during travel control execution, control targets in correspondence with the result of learning. Here the learning is to learn relationship between the travel state during normal travel based on driving operations of the driver (the behavior of the vehicle itself, such as the vehicle speed, front-rear direction acceleration, and yaw rate), and the travel environment around the vehicle.
For example, in JP-A-H7-108849, the idea is disclosed of causing a vehicle to travel automatically as follows: during normal travel based on driving operations of a driver, learning the relationship between the travel state of the vehicle and the environment state around the vehicle; and during execution of vehicle travel control, setting an environment state (control target) preferred by the driver from the results of that learning and the travel state of the vehicle, and obtaining control amounts of the vehicle so that the actual environment state becomes the control target.
However, in a related art travel control apparatuses of this kind, in reflecting preferences of the driver in travel control, simply the relationship between one travel state and one travel environment is learned, as in the relationship between the vehicle speed and an inter-vehicle distance or the relationship between the vehicle speed and the road width. Therefore the preferences of the driver cannot be fully reflected in the control results, and sometimes the driver was given a sense of incongruity.
In the above-mentioned publication, during normal travel of the vehicle, the vehicle speed and the inter-vehicle distance are sampled and preferences of the driver are learned by single regression analysis with the sampled vehicle speed as an explanatory variable and the inter-vehicle distance as a target variable. During execution of travel control, a target inter-vehicle distance is set corresponding to the present vehicle speed in accordance with that learning result. The vehicle is thereby controlled so that the inter-vehicle distance between the own vehicle and the vehicle in front becomes this target inter-vehicle distance.
Here, the target inter-vehicle distance can be set to a distance corresponding to the preferences of the driver in travel control based on learning results obtained by a single regression analysis of this kind. However, the vehicle acceleration/deceleration in a case where the inter-vehicle distance is being controlled to the target inter-vehicle distance and the responsiveness in a case where the vehicle is accelerated/decelerated cannot be made to correspond with the preferences of the driver.